Abstract 1130: Leveraging the IVIS imaging technologies for drug potency testing in orthotopic and metastatic tumor models
Bibliographic record
Abstract
Abstract Assessing anti-tumor drug potency is significantly enhanced using orthotopic and metastasis mouse tumor models. These models imitate the tumor's native environment, biological complexities, and challenges of drug delivery via blood perfusion, thereby providing a high predictive value for clinical outcomes. In this poster, we demonstrate how the IVIS optical imaging technology is employed to determine the effects of two drugs on two tumor models: •The KRAS (G12C) inhibitor, AMG510, was evaluated on the luciferase-expressing Miapaca-2 pancreatic orthotopic model •The third-generation EGFR inhibitor, AZD9291, was tested on the luciferase-expressing NIC-H1975 intracranial metastatic model Both drugs showed significant anti-tumor activity. In addition to potency testing, each drug was administered in single doses for pharmacodynamic analysis, evaluating pERK/ERK and DUSP6 levels. The pERK levels decreased after treatment, which is in line with the mechanism of action of the inhibitors. Our research illustrates that IVIS imaging offers a robust method for monitoring anti-tumor activity of drugs in orthotopic and metastatic tumor models, empowering scientists to assess new drug candidates by using biologically relevant animal models. Citation Format: Jingqi Huang, Jing Jin, Hongchen Duan, Xueqin Yang, Wenhao Jin, Yiming Zhang, Lihui Zhang, Gaoyang Xu, Liya Xie, Wentao Li. Leveraging the IVIS imaging technologies for drug potency testing in orthotopic and metastatic tumor models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1130.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".